Application of Adaptive Neuro Fuzzy Inference System (ANFIS) for Hardness Prediction of CK45 Based on Hot Rolling Parameters.
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| Title: | Application of Adaptive Neuro Fuzzy Inference System (ANFIS) for Hardness Prediction of CK45 Based on Hot Rolling Parameters. |
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| Authors: | Salimiasl, Aydin1 aydin952@gmail.com, Seidi, Esmaeil1 esmaeilseidy@yahoo.com, Moravej, Mojtaba1 moravej60@gmail.com |
| Source: | International Journal of Advanced Design & Manufacturing Technology. Autumn2025, Vol. 18 Issue 3, p51-58. 8p. |
| Subjects: | Hardness testing, Hot rolling, Prediction models, Steel, Fuzzy neural networks, Rotational motion, Mechanical behavior of materials, Deformations (Mechanics) |
| Abstract: | Rolling stands as a crucial manufacturing technique that offers the dual benefit of enhancing steel's mechanical characteristics. Given the substantial time investment and financial burden associated with rolling experiment setups, implementing predictive models for mechanical properties can enhance precision while reducing both temporal and monetary costs. This study conducted hot rolling experiments on CK45 steel across two distinct environments. The specimens underwent rolling at five different temperature levels and five varying work-roll rotation speeds, maintaining consistent reduction percentages. Following the rolling process, the samples were rapidly cooled in ambient air and cold-water conditions, with hardness measurements obtained using specialized testing equipment. The research employed the Adaptive Neuro-Fuzzy Inference approach to forecast hardness values based on operational parameters. The model utilized rolling temperature and rotational speed of the rollers as input variables, while the hardness measurements post-quenching in both air and water environments served as output data. The analysis yielded R² values exceeding 0.99 between measured and predicted results for both environments, demonstrating ANFIS's effectiveness in accurately predicting sample hardness across various rolling speeds and temperatures. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Advanced Design & Manufacturing Technology is the property of Islamic Azad University, Majlesi Branch and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 190315716 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of Adaptive Neuro Fuzzy Inference System (ANFIS) for Hardness Prediction of CK45 Based on Hot Rolling Parameters. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Salimiasl%2C+Aydin%22">Salimiasl, Aydin</searchLink><relatesTo>1</relatesTo><i> aydin952@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Seidi%2C+Esmaeil%22">Seidi, Esmaeil</searchLink><relatesTo>1</relatesTo><i> esmaeilseidy@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Moravej%2C+Mojtaba%22">Moravej, Mojtaba</searchLink><relatesTo>1</relatesTo><i> moravej60@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Advanced+Design+%26+Manufacturing+Technology%22">International Journal of Advanced Design & Manufacturing Technology</searchLink>. Autumn2025, Vol. 18 Issue 3, p51-58. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hardness+testing%22">Hardness testing</searchLink><br /><searchLink fieldCode="DE" term="%22Hot+rolling%22">Hot rolling</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Steel%22">Steel</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+neural+networks%22">Fuzzy neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Rotational+motion%22">Rotational motion</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+behavior+of+materials%22">Mechanical behavior of materials</searchLink><br /><searchLink fieldCode="DE" term="%22Deformations+%28Mechanics%29%22">Deformations (Mechanics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Rolling stands as a crucial manufacturing technique that offers the dual benefit of enhancing steel's mechanical characteristics. Given the substantial time investment and financial burden associated with rolling experiment setups, implementing predictive models for mechanical properties can enhance precision while reducing both temporal and monetary costs. This study conducted hot rolling experiments on CK45 steel across two distinct environments. The specimens underwent rolling at five different temperature levels and five varying work-roll rotation speeds, maintaining consistent reduction percentages. Following the rolling process, the samples were rapidly cooled in ambient air and cold-water conditions, with hardness measurements obtained using specialized testing equipment. The research employed the Adaptive Neuro-Fuzzy Inference approach to forecast hardness values based on operational parameters. The model utilized rolling temperature and rotational speed of the rollers as input variables, while the hardness measurements post-quenching in both air and water environments served as output data. The analysis yielded R² values exceeding 0.99 between measured and predicted results for both environments, demonstrating ANFIS's effectiveness in accurately predicting sample hardness across various rolling speeds and temperatures. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Advanced Design & Manufacturing Technology is the property of Islamic Azad University, Majlesi Branch and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 51 Subjects: – SubjectFull: Hardness testing Type: general – SubjectFull: Hot rolling Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Steel Type: general – SubjectFull: Fuzzy neural networks Type: general – SubjectFull: Rotational motion Type: general – SubjectFull: Mechanical behavior of materials Type: general – SubjectFull: Deformations (Mechanics) Type: general Titles: – TitleFull: Application of Adaptive Neuro Fuzzy Inference System (ANFIS) for Hardness Prediction of CK45 Based on Hot Rolling Parameters. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Salimiasl, Aydin – PersonEntity: Name: NameFull: Seidi, Esmaeil – PersonEntity: Name: NameFull: Moravej, Mojtaba IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Autumn2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 22520406 Numbering: – Type: volume Value: 18 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Advanced Design & Manufacturing Technology Type: main |
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